In today's business environment, enterprises continuously strive to automate and integrate their legacy information and systems. Automation may help an enterprise control its costs by reducing its resource needs and allowing it to operate in a more timely and efficient manner. Furthermore, by integrating legacy information and systems an enterprise can improve its decision making, since the information is coordinated and linked in ways not previously possible without substantial manual effort and expense.
Conventionally, attempts at integrating legacy information and systems involve accessing information from native and external data stores when it is needed and normalizing the accessed information to ready it for use. However, problems can arise when accessing information from an external data store if a non-native access method or an application-specific format needing translation is required to ready the information for use
Techniques for integrating information are often referred to as “data federation” techniques. As described above, implementing such techniques can be problematic.
However, data federation is generally considered easier to implement than integrating the processing logic of legacy systems. This is so because it is generally easier to integrate the information output of legacy systems (i.e., data federation) than to integrate the processing logic of such systems (e.g., by modifying source code).
Accordingly, existing integration techniques are focused on data federation solutions. However, these techniques are typically proprietary and ad hoc in nature. As a result, today's enterprises often have a variety of integration solutions spread throughout their computing environments. Each of these solutions must be maintained, supported, and upgraded. Therefore, today's enterprise typically dedicates a significant amount of its Information Technology resources to implementing data federation solutions.
Thus, improved techniques for federating data are needed.